IEF-YOLO: Infrared simulation image Fidelity Evaluation algorithm based on YOLO detection model

Infrared scene simulation generation technology is an important component of infrared imaging guidance hardware-in-the-loop simulation. However, there are significant differences in the infrared simulation images generated by different simulation platforms, and how to effectively analyze and evaluate the realism of infrared visual simulation generated images is a research difficulty. This paper proposes an infrared simulation image fidelity evaluation algorithm based on object detection tasks, IFE-YOLO. The model integrates Swin Transformer, YOLOX, and attention mechanism to achieve fidelity evaluation at the target task level. Firstly, we propose that STCNet backbone network extracts target feature information, which not only has Swin Transformer's excellent ability to model based on global information, but also uses attention mechanism to capture location information and channel relationship to enhance the ability to process feature information. Secondly, based on the improved PANet, a feature pyramid network is constructed for deep fusion of high-level and low-level features to effectively use semantic information and location information. Thirdly, decoupling detection head and improved position loss function are used in the target detection part to improve the model performance. Finally, the infrared fidelity evaluation process for detection task is designed, and the validity of the model is verified on the constructed infrared simulation image dataset.